The Marketer's Guide To Artificial Intelligence

Artificial intelligence (AI) is surging in ad/mar tech land. Or resurging, depending on how good your memory is.

IBM continues to push Watson, and, in the run-up to their respective conferences, Salesforce and Oracle talked up their own AI initiatives. Also, Google, Facebook, IBM, Microsoft and Amazon banded together to create best practices around AI technologies.

And startups like Adgorithms, Boomtrain, Cognitiv, Kenshoo, Lattice Engines, Rocket Fuel and numerous others continue to extol the virtues of their AI-powered applications.

Unfortunately, AI has become an umbrella term in the marketing/advertising world. Santanu Kolay, SVP of engineering at ad tech company Turn, noted in a Friday column that despite the marketing hype and advancements, we are still well away from a true, fully automated AI system that requires no human assistance.

“The trouble with AI [in enterprise tech] is it’s defined however anybody wants to,” said Gartner research VP Martin Kihn. “Some people use it as a synonym for machine learning.” (Machine learning is software that learns from experience.)

But machine learning, and other tech bundled under the AI banner, like deep learning, natural language processing and natural language generation, are actually just the “ingredients,” said Stephen Gold, CMO of IBM’s Watson Group.

While many of these ingredients have been used by enterprises for a while, their importance in marketing applications is growing – especially so with machine learning.

“The idea of using machine learning and AI is driven by the complexity of where we are right now,” said Joe Stanhope, VP and principal analyst at Forrester. “There’s too much data. Marketing departments can’t deliver the analytics and deploy that level of agility that customers require. We’re reaching the limits of human cognitive power.”

How Are Tech Companies Using AI?

Marketing tech and ad tech vendors have, grossly generalizing, two strategies when integrating AI into their offerings. (For the purposes of simplicity, I’m going to use “AI” as a catch-all.)

There’s the purpose-driven approach, which we see from companies like Rocket Fuel or Boomtrain, who use machine learning to power very specific marketing or advertising processes. This philosophy isn’t exclusive to startups. Google, for instance, applies its machine learning to analytics. (It also has the consumer-facing application Google Assistant, but that’s a separate story.)

Then there’s the platform approach, championed by tech giants like IBM and Salesforce, who want to use machine learning to power processes across a range of enterprise functions, many of which go well beyond marketing.

These applications tend to revolve around image or speech/text recognition, which were the early academic focus areas.

When Salesforce introduced its Einstein platform in September for instance, it showcased how the system could find product images in social media feeds. And IBM built Watson around the ability to comprehend unstructured human speech and debuted it on the game show “Jeopardy.” That’s why early business applications focused on answering medical or legal questions.

What follows is a partial list of vendors, in no particular order, that offer AI for marketing or advertising.

Platform AI

Vendors that take a platform-based approach to AI emphasize that they want to make it easier for clients to harness its power.

“AI is out of reach for the vast majority of companies,” said John Ball, GM of Salesforce’s Einstein, during a September conference call. He said Einstein would “democratize” AI such that any business could build machine-learning applications.

IBM’s rhetoric around Watson is similar.

“A lot of companies, don’t have that skill [to build machine-learning applications], so they need to contract someone or hire people,” said IBM’s Gold, adding that Watson enables “the average Joe” to access “higher-fidelity AI conversations,” like chatbots.

IBM

“Our strategy is simple,” said Maria Winans, CMO of IBM’s commerce unit. “It’s looking at this whole [marketing] space and asking what are the right set of capabilities we want to bring Watson in to augment.”

IBM doesn’t have a catalog of out-of-the-box Watson applications; instead, IBM clients can level up an existing IBM marketing cloud function with AI. (IBM has integrated parts of its Weather Co. acquisition with Watson.)

“If you want to take your campaigns to the next level, like bringing in contextual real-time personalization, it’s very easy to expand,” Winans said. “If you want to bring in Watson capabilities, we’re making it easy for our customers within their solutions.”

Winans pointed to an application for The North Face, which harnesses Watson’s language comprehension to help consumers find the perfect jacket. She also noted that Watson can help marketers segment and design customer journeys.

“And then you can personalize, using machine learning to identify the best offers and content at scale,” Winans said. “You can project goals and trigger an alert when something isn’t working, with recommendations on how to improve it.”

Some are skeptical about Watson’s utility for marketing and advertising.

“Marketers don’t use it as much as it’s talked about,” said Gartner’s Kihn. “But it does have some services marketers could use that are cool. You could send Watson something a consumer has written, and it can analyze a personality type based on the content. But it’s all very one-off.”

But maybe marketers just need more time to figure out how and where to use Watson. Rocket Fuel CTO Mark Torrance, who researched AI during his education at Stanford and MIT, was impressed after tooling with Watson at a hackathon. (Rocket Fuel is also an IBM partner.) He worked with a news API that crawls hundreds of thousands of websites and annotates them semantically.

Torrance and his team built a proof-of-concept application that automatically found concepts negatively associated with a brand (airline travel and the Samsung Note 7, as a hypothetical) and avoided showing ads near those concepts.

Salesforce

Salesforce is the other giant taking a broad, platform-based approach with Einstein. Like Watson, Einstein is a software upgrade. If a client wants to enhance its Salesforce Marketing Cloud applications, it can add Einstein’s intelligence.

“Salesforce is putting a stake in the ground and it has a grand vision,” said Kihn. “The image recognition is pretty cool, and marketers could use it, though it’s hard to see how it would work in [Salesforce’s workflow product] Journey Builder. It’s a vision and not a marketer-ready product."

“That’s where we use machine learning to create a propensity score to each individual contact record,” said Marketing Cloud SVP Eric Stahl. “We can measure the propensity to open an email, to click on something, to purchase and to unsubscribe. We can use that propensity score to build segments.”

Salesforce’s VP of ad products, Liam Doyle, added that Einstein has been used to build a product recommendation engine.

Purpose-driven AI

Those who build AI to power specific applications are typically smaller companies (Google excepted, of course) and argue that the platform-based approach is simply too broad to be useful. Machine learning, in other words, isn’t one-size-fits-all.

Shashi Upadhyay, CEO of Lattice Engines, which uses machine learning to predict whether consumers will make a purchase, said there are limited marketing applications for a system like IBM’s Watson.

“It came from the angle of memorizing large quantities of text,” he said. “You ask it a question, it gives an answer. For those kinds of problems, Watson is very good. But in sales and marketing, it’s never about memorization. It’s about predicting what someone will do in an uncertain environment. And Watson isn’t good at that kind of stuff.”

Upadhyay and his peers emphasize the importance of using the right machine-learning algorithm – or the right combination of algorithms – for specific applications. As such, many emphasize interoperability, claiming their proprietary AI engines can be applied to other marketing clouds.

Google

Google’s AI applications are centered around two different analytics offerings.

“[Automated Insights] is large-scale machine learning,” said Babak Pahlavan, senior director of product management at Google, “deployed to understand and showcase patterns that [businesses] should pay attention to, both in terms of anomalies and opportunities.”

And Google is working on applications around what Pahlavan calls “conversational analytics,” to build a system that, like Watson, can respond to specific business questions in real time.

One of Lattice’s strengths, said Upadhyay, is its collective learnings from numerous marketing systems. “It’s learning from Salesforce, Adobe, Eloqua and external data,” he explained. And the more data an AI system can access, the better its predictions will be.

Lattice also has an explicit focus, focusing on use cases around finding and prioritizing new leads, cross-sell/up-sell, retention and database cleansing.

Rocket Fuel

Rocket Fuel was among the first ad tech firms to refer to "artificial intelligence" in its pitch to clients. In 2012, its former CEO, George John, deployed the term frequently on client pitches and, later, earnings calls.

In the post-John era, the company still talks up its AI approach – if at a somewhat lower volume. Rocket Fuel uses AI to help clients optimize ad spend and ad placements. CTO Torrance described the different levels of sophistication that need to be applied to different use cases.

A basic, non-AI model would be a simple if-then: If a person visits a branded site, then the advertiser will pay more for ads. A more sophisticated algorithm might state that if a person visits a site more often or for a longer period of time, the advertiser will bid more to place the ad.

“It’s fancier,” Torrance said, “because it’s not a decision tree with just one branch: Were you there or weren’t you?”

As more factors influence pricing decisions (i.e., how many products did the consumer browse?), the campaign becomes less manageable for a human, which is when the software steps in. A basic system, however, would treat each of these factors independently.

More sophisticated algorithms would consider the gestalt – how all the different factors interact with each other – to influence a pricing decision. So the machine-learning apparatus can educate itself based on numerous consumer actions, rather than just one or two.

Adgorithms

Before Salesforce rolled out Einstein, Adgorithms had its AI engine, Albert, which it uses to power marketing in email, mobile, search, social and display advertising.

“There are two stages: the integration and onboarding side,” said founder and CEO Or Shani. “We tailor the software to your needs. If you’re a client, we interview you about your business: margins, business expectations, general approach in terms of whether you want to be more aggressive or conservative. We integrate it to the CRM and analytics and everything.”

And integration, Shani claimed, can be quick – taking from one day to eight months, depending on how much data the client allows Albert to ingest.

“It’s short because Albert can work with parts of the data,” Shani said. “We don’t need to integrate with everything. We can work with just specific pieces of information, through the API or by you sending us an Excel sheet. The more info you plug in, the better it becomes.”

Shani initially expected Adgorithms to service the midmarket, but the company is starting to scale up, working with larger clients and agencies. (It has a little less than 300 clients, according to Shani, half of which are brand-direct.)

Cognitiv

Cognitiv launched this year promising to use deep learning to automatically create custom ad buying algorithms.

When AppNexus started the "bring your own algorithm" trend, said CEO Jeremy Fain, advertisers and agencies had to create those algos using AppNexus' proprietary coding language called Bonsai.

Fain said Cognitiv takes huge datasets, pushes it through "a neural network engine," which then produces a neural network that predicts the likelihood of an outcome like a click or conversion.

"Once you've built a system like Cognitiv it can just keep pumping out new and evolved neural networks," he explained.

This approach is well-suited to programmatic because of the amount of data involved. Every exchange sends every impression to every DSP, so every DSP basically has the same data from an input perspective. The company partners with The Trade Desk and AppNexus and other firms that have a vendor ecosystem and lots of API integrations.

Among Cognitiv's customers is an insurance company, which the company did not have permission to identify, that asked it to score 55 million users for likelihood to convert.

"Our algorithm was both more efficient and accurate than in-house agency data science team," Fain said of the use case. "You don't have to have the data talent anymore. The big trend we're ultimately solving regardless of deep learning is custom buying algorithms."

Boomtrain

Boomtrain is essentially a marketing cloud. It started by building out the AI, said CEO Nick Edwards, then the applications on top of it.

“We spend most of the focus on the brain,” Edwards said. “The execution layer is the necessary part of the platform because marketers don’t want to buy an AI platform that needs API access. They want an API platform that can drive real business results today.”

Boomtrain, which has about 150 mid- and upper-midmarket clients in retail, media and publishing (it works with Forbes and CBS Interactive), focuses on marketing executions as opposed to paid media.

Like Adgorithms, the Boomtrain heavily customizes its solution on a customer-by-customer basis.

“Rather than apply one-size-fits-all, we’re testing and measuring dozens of different machine-learning approaches and multivariate testing those on a per-customer basis to optimize the AI for that individual customer,” Edwards said.

And while all of Boomtrain’s learning algorithms are pre-made, customization comes in the combination.

“It’s about understanding the right algorithmic combination to optimize for individual customers’ business objectives,” Edwards said.

An algorithm that performs semantic analysis, for instance, is necessary if a campaign needs machine reading that relates different pieces of content to each other.

“There are plenty of machine-learning algorithms out there,” Edwards said. “The hard part is understanding which algorithms are optimal for a given client.”

1 Comment

Great article Ryan and agree it's amazing how rapidly purpose-driven AI in particular is advancing. And since perhaps the most critical “purpose” for any business is engaging prospective customers, I’d like to humbly point out that my company, Conversica, offers a true AI-based automated sales assistant who engages potential customers in natural, two-way human conversations, and tirelessly reaches out to every single lead, as many times and over as long a timespan as is required. She’s always persistent, always polite, and empowers the human salespeople to focus on selling and closing deals instead of chasing down leads. Sorry for the pitch but it really is a good example of what you described and, honestly, I find this stuff mind-blowing.